MOTIVATION: A typical goal in gene expression studies is identifying certain gene sets enriched with significant genes. The measurement of many gene expression experiments for several concentrations or time points allows the modeling of the concentration/time-response relationship for each gene, and the subsequent estimation of a gene-wise alert. In this work, an approach is proposed to transfer the concept of alerts from single genes to gene sets, yielding a global significance statement and the respective concentration or time where the first enrichment of the gene set can be observed. The methodology is based on a Kolmogorov-Smirnoff type test statistic for each gene set. RESULTS: Simulations show that a majority of these sets can be identified especially for lower numbers of true gene sets with a signal. The false positive rate can be controlled by subsequent decorrelation approaches. Overall, the true gene set-wise alerts are rarely overestimated and rather tend to be underestimated. AVAILABILITY AND IMPLEMENTATION: The code needed to reproduce the simulations and apply the AlertGS methodology is available at the GitHub repository: https://github.com/FKappenberg/AlertGS.

MOTIVATION: An important goal of concentration-response studies in toxicology is to determine an 'alert' concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest. RESULTS: In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance. AVAILABILITY AND IMPLEMENTATION: The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.